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three-dimensional-memory三度空间记忆

Agent Skill

three-dimensional-memory 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:three-dimensional-memory(三度空间记忆)
来源仓库:https://github.com/amlyx/three-dimensional-memory
安装命令:
openclaw skills install three-dimensional-memory
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

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ClawHubOpenClaw
openclaw skills install three-dimensional-memory

简介

通过跨三个维度组织文件来管理 AI 助手内存:时间线日志、完整对话备份和基于主题的项目文件夹,以便轻松查看

SKILL.md

three-dimensional-memory

Three-Dimensional Memory System for AI Assistants — 人类思维方式的文件记忆管理

A memory management system that mirrors human cognition: organized by time, conversation, and topics.


🎯 Why This Skill?

Traditional file management organizes by file type (docs, images, videos). Humans don't think that way.

Humans remember:

  • When it happened (time)
  • What was said (conversation)
  • What it was about (topic)

This skill creates a three-dimensional memory space for AI assistants and their users.


🧠 The Three Dimensions

Dimension 1: Timeline Memory

memory/
├── 2026-02-21.md    ← What happened today
├── 2026-02-22.md    ← What happened today
└── 2026-02-23.md    ← What happened today

Purpose: Daily work logs, chronological record of events Update frequency: Daily Content: Tasks completed, decisions made, meetings held


Dimension 2: Conversation Stream

AI-memory-backup/
├── backup-20260221.md    ← Complete conversation transcript
├── backup-20260222.md    ← Complete conversation transcript
└── backup-20260223.md    ← Complete conversation transcript

Purpose: Full context preservation, searchable dialogue history Update frequency: Per conversation Content: Every word exchanged, including user messages and AI responses


Dimension 3: Topic Network

topic-memory/
├── project-product-launch/
│   ├── proposal-v1.md
│   ├── proposal-v2.md
│   └── final-version.md
│
├── decision-org-restructure/
│   ├── options-considered.md
│   ├── final-decision.md
│   └── implementation-plan.md
│
└── knowledge-market-analysis/
    ├── competitor-research.md
    └── trend-report.md

Purpose: Project-centric information aggregation Update frequency: As projects evolve Content: All documents, decisions, and knowledge related to a specific topic


📁 Recommended File Structure

workspace/
│
├── memory/                      ← Dimension 1: Timeline
│   ├── 2026-02-21.md
│   ├── 2026-02-22.md
│   └── 2026-02-23.md
│
├── AI-memory-backup/            ← Dimension 2: Conversation
│   ├── backup-20260221.md
│   ├── backup-20260222.md
│   └── backup-20260223.md
│
├── topic-memory/                ← Dimension 3: Topics
│   ├── project-[name]/
│   ├── decision-[name]/
│   ├── system-[name]/
│   └── knowledge-[name]/
│
├── skills/                      ← AI capabilities
│
├── MEMORY.md                    ← AI's long-term identity
├── SOUL.md                      ← AI's personality
└── USER.md                      ← User preferences

🚀 Quick Start

Step 1: Initialize Structure

Create the three directories in your workspace:

mkdir -p memory
mkdir -p AI-memory-backup
mkdir -p topic-memory

Step 2: Daily Workflow

Every day, the AI should:

  1. Write to memory/YYYY-MM-DD.md

- Summary of today's work - Decisions made - Tasks completed

  1. Backup conversation to AI-memory-backup/backup-YYYYMMDD.md

- Full transcript - Important context - User preferences learned

  1. Update relevant topic-memory/ folders

- Add new documents to projects - Update decision records - Consolidate knowledge

Step 3: Access Information

Find by time: "What did we do on Monday?" → Check memory/

Find by context: "What exactly did I say about pricing?" → Check AI-memory-backup/

Find by topic: "Where's the product launch plan?" → Check topic-memory/project-product-launch/


📝 Example: A Day in Three Dimensions

Scenario: Product Strategy Meeting

Dimension 1 - memory/2026-02-23.md:

# 2026-02-23 Work Log

## Morning
- Product strategy meeting with CEO
- Decided on three-tier pricing model
- Delayed launch by 2 weeks for additional testing

## Afternoon  
- Drafted pricing proposal
- Created financial projections
- Scheduled follow-up for tomorrow

## Decisions
- ✅ Adopt tiered pricing (Basic/Pro/Enterprise)
- ✅ Delay launch from March 1 → March 15
- ❌ Do not offer early-bird discounts

Dimension 2 - AI-memory-backup/backup-20260223.md:

# Conversation Backup - 2026-02-23

## Product Pricing Discussion

User: "We need to decide on pricing today."

AI: "What are you considering?"

User: "I'm thinking three tiers: $29, $99, $299"

AI: "Have you considered the psychology of pricing? 
      $29 might signal 'cheap', $299 signals 'premium'."

User: "Good point. Let's go with $39, $99, $299"
[Full conversation continues...]

Dimension 3 - topic-memory/project-product-launch/:

# Product Launch Project

## pricing-strategy.md (updated today)
Final decision: Three-tier model
- Starter: $39/month
- Professional: $99/month  
- Enterprise: $299/month

## timeline.md (updated today)
Launch date: March 15, 2026 (delayed from March 1)

## key-decisions.md
- Pricing tiers finalized (2026-02-23)
- Launch delayed for QA (2026-02-23)

💡 Best Practices

For Users

  1. Review daily logs weekly - Quick scan of what happened
  2. Search conversation backups - Find exact quotes and context
  3. Use topic folders - Navigate by project, not by file type
  4. Keep MEMORY.md updated - AI's identity and your preferences

For AI Assistants

  1. Update all three dimensions daily - Don't skip any
  2. Be consistent with naming - Use clear, searchable topic names
  3. Cross-reference - Link between dimensions when relevant
  4. Maintain the index - Keep a master index of active topics

🔍 Troubleshooting

"I can't find a file" → Check all three dimensions. If it's not in timeline or topic, search conversation backup.

"There's duplicate information" → That's by design! Timeline shows when, topic shows what, conversation shows why.

"The AI forgot what we discussed" → Check AI-memory-backup/. The full context is there.


🌟 Why It Works

Traditional file management: "Where did I save that document?"

Three-dimensional memory: "We discussed pricing in yesterday's meeting" → Check memory/2026-02-23.md → Find reference to topic-memory/project-pricing/ → Open latest version

Result: Find files in 10 seconds instead of 5 minutes.


📄 Metadata

  • Author: @openclaw-user
  • Created: 2026-02-23
  • Version: 1.0.0
  • License: MIT
  • Tags: memory, organization, productivity, workflow

*"The best file system is the one you don't have to think about."*

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

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